Atik Wintarti, Fadhilah Qalbi Annisa, Hasanuddin Al-Habib, Riskyana Dewi Intan Puspitasari
Student learning evaluations in the Department of Data Science at Universitas Negeri Surabaya (Unesa) have shown that student academic achievement results can be further improved. It is based on students' average scores on quiz courses, Midterm Exams, and Final Exams. To enhance students' future academic achievement, it is essential to assess their learning abilities by considering a range of cognitive and non-cognitive factors. Data collection was conducted among students in the Unesa Data Science Undergraduate Study Program, cohort 2022. The questionnaire was prepared using Google Forms to make it easier to create and fill out, as well as to process the data. The data were then processed using a Python program, and analysis was carried out using regression method i.e. Elastic Net. The results of the research show the factors that influence learning achievements are non-cognitive, such as the mother's educational background, the marital status of the biological parents, the time spent on social media and studying, past experiences with trauma and suicide attempts, knowledge of the data science major, monthly expenses, participation in competitions and research, and where students typically studied. The findings can be used to design further lecture activities that aim to overcome or mitigate factors negatively impacting academic achievement, for example reduce social media activities, focus on data science major, and often follow competition and research. © 2025 IEEE.
Universitas Negeri Surabaya, Department of Data Science, Surabaya, Indonesia; Universitas Negeri Surabaya, Department of Artificial Intelligence, Surabaya, Indonesia
